This function takes an object of class iCellR and and runs kNet for dimensionality reduction.
pseudotime.knetl(
x = NULL,
dist.method = "euclidean",
k = 5,
abstract = TRUE,
data.type = "pca",
dims = 1:20,
conds.to.plot = NULL,
my.layout = "layout_with_fr",
node.size = 10,
cluster.membership = FALSE,
interactive = TRUE,
node.colors = NULL,
edge.color = "gray",
out.name = "Pseudotime.Abstract.KNetL",
my.seed = 1
)An object of class iCellR.
the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "mandatattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".
KNN the higher the number the less sensitivity, default = 5.
Draw all the cells or clusters, , default = TRUE.
Choose between "tsne", "pca", "umap", default = "pca". We highly recommend PCA.
PCA dimentions to be use for clustering, default = 1:20.
Choose the conditions you want to see in the plot, default = NULL (all conditions).
Choose a layout, default = "layout_with_fr".
Size of the nodes, , default = 10.
Calculate memberships based on distance.
If set to TRUE an interactive HTML file will be created, default = TRUE.
Color of the nodes, default = random colors.
Solor of the edges, default = "gray".
If "interactive" is set to TRUE, the out put name for HTML, default = "Abstract.KNetL".
seed number, default = 1.
A plot.